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Volumn , Issue , 2005, Pages 1-323

Decomposition methodology for knowledge discovery and data mining: Theory and applications

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EID: 85115728351     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/5686     Document Type: Book
Times cited : (38)

References (345)
  • 1
    • 0025725905 scopus 로고
    • Instancebased learning algorithms
    • Aha, D. W.; Kibler, D.; and Albert, M. K., Instancebased learning algorithms. Machine Learning 6(1):37-66, 1991
    • (1991) Machine Learning , vol.6 , Issue.1 , pp. 37-66
    • Aha, D.W.1    Kibler, D.2    Albert, M.K.3
  • 2
    • 0029478402 scopus 로고
    • A tabu search approach to the clustering problem
    • Al-Sultan K. S., A tabu search approach to the clustering problem, Pattern Recognition, 28:1443-1451, 1995
    • (1995) Pattern Recognition , vol.28 , pp. 1443-1451
    • Al-Sultan, K.S.1
  • 3
    • 0030571816 scopus 로고    scopus 로고
    • Computational experience on four algorithms for the hard clustering problem
    • Al-Sultan K. S., Khan M. M.: Computational experience on four algorithms for the hard clustering problem. Pattern Recognition Letters 17(3): 295-308, 1996.
    • (1996) Pattern Recognition Letters , vol.17 , Issue.3 , pp. 295-308
    • Al-Sultan, K.S.1    Khan, M.M.2
  • 4
    • 0030235637 scopus 로고    scopus 로고
    • Error Reduction through Learning Multiple Descriptions
    • Ali K. M., Pazzani M. J., Error Reduction through Learning Multiple Descriptions. Machine Learning, 24: 3, 173-202, 1996.
    • (1996) Machine Learning , vol.24 , Issue.3 , pp. 173-202
    • Ali, K.M.1    Pazzani, M.J.2
  • 5
    • 0030170591 scopus 로고    scopus 로고
    • An Efficient Algorithm for Optimal Pruning of Decision Trees
    • Almuallim H., An Efficient Algorithm for Optimal Pruning of Decision Trees. Artificial Intelligence 83(2): 347-362, 1996.
    • (1996) Artificial Intelligence , vol.83 , Issue.2 , pp. 347-362
    • Almuallim, H.1
  • 6
    • 0028496468 scopus 로고
    • Learning Boolean concepts in the presence of many irrelevant features
    • Almuallim H,. and Dietterich T.G., Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69: 1-2, 279-306, 1994.
    • (1994) Artificial Intelligence , vol.69 , Issue.1-2 , pp. 279-306
    • Almuallim, H.1    Dietterich, T.G.2
  • 8
    • 0029183827 scopus 로고
    • Efficient classification for multiclass problems using modular neural networks
    • Anand R, Methrotra K, Mohan CK, Ranka S. Efficient classification for multiclass problems using modular neural networks. IEEE Trans Neural Networks, 6(1): 117-125, 1995.
    • (1995) IEEE Trans Neural Networks , vol.6 , Issue.1 , pp. 117-125
    • Anand, R.1    Methrotra, K.2    Mohan, C.K.3    Ranka, S.4
  • 12
    • 0033556929 scopus 로고    scopus 로고
    • Boosted Mixture of Experts: An ensemble learning scheme
    • Avnimelech R. and Intrator N., Boosted Mixture of Experts: an ensemble learning scheme, Neural Computations, 11(2):483-497, 1999.
    • (1999) Neural Computations , vol.11 , Issue.2 , pp. 483-497
    • Avnimelech, R.1    Intrator, N.2
  • 13
    • 85115708529 scopus 로고
    • In Proceedings of the Third International Joint Conference on Pattern Recognition, pages 45-49, San Diego, CA
    • Baker E., and Jain A. K., On feature ordering in practice and some finite sample effects. In Proceedings of the Third International Joint Conference on Pattern Recognition, pages 45-49, San Diego, CA, 1976.
    • (1976) On feature ordering in practice and some finite sample effects
    • Baker, E.1    Jain, A.K.2
  • 14
    • 0027453616 scopus 로고
    • Model-based Gaussian and non-Gaussian clustering
    • Banfield J. D. and Raftery A. E.. Model-based Gaussian and non-Gaussian clustering. Biometrics, 49:803-821, 1993.
    • (1993) Biometrics , vol.49 , pp. 803-821
    • Banfield, J.D.1    Raftery, A.E.2
  • 15
    • 0002094343 scopus 로고    scopus 로고
    • In “Advances in Kernel Methods, Support Vector Learning”, Bernhard Scholkopf, Christopher J. C. Burges, and Alexander J. Smola (eds), MIT Press, Cambridge, USA
    • Bartlett P. and Shawe-Taylor J., Generalization Performance of Support Vector Machines and Other Pattern Classifiers, In “Advances in Kernel Methods, Support Vector Learning”, Bernhard Scholkopf, Christopher J. C. Burges, and Alexander J. Smola (eds), MIT Press, Cambridge, USA, 1998.
    • (1998) Generalization Performance of Support Vector Machines and Other Pattern Classifiers
    • Bartlett, P.1    Shawe-Taylor, J.2
  • 16
    • 0001931577 scopus 로고    scopus 로고
    • An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
    • Bauer, E. and Kohavi, R., “An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants”. Machine Learning, 35: 1-38, 1999.
    • (1999) Machine Learning , vol.35 , pp. 1-38
    • Bauer, E.1    Kohavi, R.2
  • 17
    • 0000254593 scopus 로고
    • Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion
    • Baxt, W. G., Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Computation, 2(4):480-489, 1990.
    • (1990) Neural Computation , vol.2 , Issue.4 , pp. 480-489
    • Baxt, W.G.1
  • 18
    • 33744958288 scopus 로고    scopus 로고
    • Nearest neighbor classification from multiple feature subsets
    • Bay, S., Nearest neighbor classification from multiple feature subsets. Intelligent Data Analysis, 3(3): 191-209, 1999.
    • (1999) Intelligent Data Analysis , vol.3 , Issue.3 , pp. 191-209
    • Bay, S.1
  • 20
    • 0017933553 scopus 로고
    • Myopic policies in sequential classification
    • February
    • BenBassat M., Myopic policies in sequential classification. IEEE Trans, on Computing, 27(2):170-174, February 1978.
    • (1978) IEEE Trans, on Computing , vol.27 , Issue.2 , pp. 170-174
    • BenBassat, M.1
  • 23
    • 0017931947 scopus 로고    scopus 로고
    • Fast algorithms for constructing minimal spanning trees in coordinate spaces
    • February 1978, 275
    • Bentley J. L. and Friedman J. H., Fast algorithms for constructing minimal spanning trees in coordinate spaces. IEEE Transactions on Computers, C27(2):97-105, February 1978. 275
    • IEEE Transactions on Computers , vol.C27 , Issue.2 , pp. 97-105
    • Bentley, J.L.1    Friedman, J.H.2
  • 24
    • 0020300879 scopus 로고
    • Decision trees and diagrams
    • Bernard M.E., Decision trees and diagrams. Computing Surveys, 14(4):593-623, 1982.
    • (1982) Computing Surveys , vol.14 , Issue.4 , pp. 593-623
    • Bernard, M.E.1
  • 26
    • 0002483047 scopus 로고    scopus 로고
    • Data Mining by Decomposition: Adaptive Search for Hypothesis Generation
    • Bhargava H. K., Data Mining by Decomposition: Adaptive Search for Hypothesis Generation, INFORMS Journal on Computing Vol. 11, Iss. 3, pp. 239-47, 1999.
    • (1999) INFORMS Journal on Computing , vol.11 , Issue.3 , pp. 239-247
    • Bhargava, H.K.1
  • 28
    • 0031334221 scopus 로고    scopus 로고
    • Blum, A. L. and Langley, P., 1997, Selection of relevant features and examples in machine learning, Artificial Intelligence, 97, pp.245-271.
    • (1997) , vol.97 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 33
    • 0028443644 scopus 로고
    • Trading accuracy for simplicity in decision trees
    • Bratko I., and Bohanec M., Trading accuracy for simplicity in decision trees. Machine Learning 15: 223-250, 1994.
    • (1994) Machine Learning , vol.15 , pp. 223-250
    • Bratko, I.1    Bohanec, M.2
  • 35
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L., Bagging predictors. Machine Learning, 24(2):123-140, 1996
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 40
    • 0035788924 scopus 로고    scopus 로고
    • Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, San Diego, USA
    • Buja, A. and Lee, Y.S., Data mining criteria for tree based regression and classification, Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, (pp 27-36), San Diego, USA, 2001.
    • (2001) Data mining criteria for tree based regression and classification , pp. 27-36
    • Buja, A.1    Lee, Y.S.2
  • 41
    • 0003637516 scopus 로고
    • Doctoral dissertation. School of Computing Science, University of Technology. Sydney. Australia
    • Buntine, W., A Theory of Learning Classification Rules. Doctoral dissertation. School of Computing Science, University of Technology. Sydney. Australia, 1990.
    • (1990) A Theory of Learning Classification Rules
    • Buntine, W.1
  • 42
    • 0002745636 scopus 로고    scopus 로고
    • in U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pp, AAAI/MIT Press
    • Buntine, W., “Graphical Models for Discovering Knowledge”, in U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pp 59-82. AAAI/MIT Press, 1996.
    • (1996) Graphical Models for Discovering Knowledge , pp. 59-82
    • Buntine, W.1
  • 43
    • 0002117591 scopus 로고
    • A Further Comparison of Splitting Rules for Decision-Tree Induction
    • Buntine W., Niblett T., A Further Comparison of Splitting Rules for Decision-Tree Induction. Machine Learning, 8: 75-85, 1992.
    • (1992) Machine Learning , vol.8 , pp. 75-85
    • Buntine, W.1    Niblett, T.2
  • 44
    • 77956256481 scopus 로고    scopus 로고
    • W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, pages 788-797. Oxford University Press
    • Buczak A. L. and Ziarko W., “Neural and Rough Set Based Data Mining Methods in Engineering”, Klosgen W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, pages 788-797. Oxford University Press, 2002.
    • (2002) Neural and Rough Set Based Data Mining Methods in Engineering
    • Buczak, A.L.1    Ziarko, W.2
  • 49
    • 0030681095 scopus 로고    scopus 로고
    • On the Accuracy of Meta-learning for Scalable Data Mining, J
    • Chan P.K. and Stolfo S.J, On the Accuracy of Meta-learning for Scalable Data Mining, J. Intelligent Information Systems, 8:5-28, 1997.
    • (1997) Intelligent Information Systems , vol.8 , pp. 5-28
    • Chan, P.K.1    Stolfo, S.J.2
  • 51
    • 0000291808 scopus 로고    scopus 로고
    • Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification
    • Chen K., Wang L. and Chi H., Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification. International Journal of Pattern Recognition and Artificial Intelligence, 11(3): 417-445, 1997.
    • (1997) International Journal of Pattern Recognition and Artificial Intelligence , vol.11 , Issue.3 , pp. 417-445
    • Chen, K.1    Wang, L.2    Chi, H.3
  • 53
    • 85115669293 scopus 로고    scopus 로고
    • In Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Workshop, Thirteenth National Conference on Artificial Intelligence. Portland, OR: AAAI Press
    • Cherkauer, K. J., Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks. In Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Workshop, Thirteenth National Conference on Artificial Intelligence. Portland, OR: AAAI Press, 1996.
    • (1996) Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks
    • Cherkauer, K.J.1
  • 55
    • 34249966007 scopus 로고
    • The CN2 rule induction algorithm
    • Clark P., and Niblett T., The CN2 rule induction algorithm. Machine Learning, 3:261-284, 1989.
    • (1989) Machine Learning , vol.3 , pp. 261-284
    • Clark, P.1    Niblett, T.2
  • 56
    • 85015191605 scopus 로고
    • quot; In Proceedings of the European Working Session on Learning, pp, Pitman
    • Clark, P. and Boswell, R., “Rule induction with CN2: Some recent improvements.” In Proceedings of the European Working Session on Learning, pp. 151-163, Pitman, 1991.
    • (1991) Rule induction with CN2: Some recent improvements , pp. 151-163
    • Clark, P.1    Boswell, R.2
  • 57
    • 85115729292 scopus 로고
    • In Proceedings of the Sixth International Workshop on Machine Learning, San Mateo CA:, pp, Morgan Kaufmann
    • Clearwater, S., T. Cheng, H. Hirsh, and B. Buchanan. Incremental batch learning. In Proceedings of the Sixth International Workshop on Machine Learning, San Mateo CA:, pp. 366-370. Morgan Kaufmann, 1989.
    • (1989) Incremental batch learning , pp. 366-370
    • Clearwater, S.1    Cheng, T.2    Hirsh, H.3    Buchanan, B.4
  • 58
    • 0024715745 scopus 로고
    • Extensions to the CART algorithm
    • August
    • Crawford S. L., Extensions to the CART algorithm. Int. J. of ManMachine Studies, 31(2):197-217, August 1989.
    • (1989) Int. J. of ManMachine Studies , vol.31 , Issue.2 , pp. 197-217
    • Crawford, S.L.1
  • 60
    • 0034824884 scopus 로고    scopus 로고
    • Concept Decomposition for Large Sparse Text Data Using Clustering
    • Dhillon I. and Modha D., Concept Decomposition for Large Sparse Text Data Using Clustering. Machine Learning. 42, pp.143-175. (2001).
    • (2001) Machine Learning. , vol.42 , pp. 143-175
    • Dhillon, I.1    Modha, D.2
  • 62
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Dietterich, T. G., “Approximate statistical tests for comparing supervised classification learning algorithms”. Neural Computation, 10(7): 1895-1924, 1998.
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1924
    • Dietterich, T.G.1
  • 63
    • 0034250160 scopus 로고    scopus 로고
    • An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging
    • Dietterich, T. G., An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging. Boosting and Randomization. 40(2):139-157, 2000.
    • (2000) Boosting and Randomization. , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 64
    • 85115725640 scopus 로고    scopus 로고
    • In J. Kittler and F. Roll, editors, First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, pages 1-15. Springer-Verlag
    • Dietterich T., Ensemble methods in machine learning. In J. Kittler and F. Roll, editors, First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, pages 1-15. Springer-Verlag, 2000
    • (2000) Ensemble methods in machine learning
    • Dietterich, T.1
  • 65
    • 0000406788 scopus 로고
    • Solving multiclass learning problems via error-correcting output codes
    • Dietterich, T. G., and Ghulum Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
    • (1995) Journal of Artificial Intelligence Research , vol.2 , pp. 263-286
    • Dietterich, T.G.1    Bakiri, G.2
  • 69
  • 70
    • 0031269184 scopus 로고    scopus 로고
    • On the Optimality of the Naive Bayes Classifier under Zero-One Loss
    • Domingos, P., & Pazzani, M., On the Optimality of the Naive Bayes Classifier under Zero-One Loss. Machine Learning, 29: 2, 103-130, 1997.
    • (1997) Machine Learning , vol.29 , Issue.2 , pp. 103-130
    • Domingos, P.1    Pazzani, M.2
  • 75
    • 0002101112 scopus 로고    scopus 로고
    • In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthu-rusamy editors., Advances in Knowledge Discovery and Data Mining, pp, AAAI/MIT Press
    • Elder I. and Pregibon, D., “A Statistical Perspective on Knowledge Discovery in Databases”, In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthu-rusamy editors., Advances in Knowledge Discovery and Data Mining, pp. 83-113, AAAI/MIT Press, 1996.
    • (1996) A Statistical Perspective on Knowledge Discovery in Databases , pp. 83-113
    • Elder, I.1    Pregibon, D.2
  • 77
    • 85170282443 scopus 로고    scopus 로고
    • In E. Simoudis, J. Han, and U. Fayyad, editors, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226-231, Menlo Park, CA, AAAI, AAAI Press
    • Ester M., Kriegel H.P., Sander S., and Xu X., A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han, and U. Fayyad, editors, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226-231, Menlo Park, CA, 1996. AAAI, AAAI Press.
    • (1996) A density-based algorithm for discovering clusters in large spatial databases with noise
    • Ester, M.1    Kriegel, H.P.2    Sander, S.3    Xu, X.4
  • 80
    • 0012310023 scopus 로고    scopus 로고
    • In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds), Advances in Knowledge Discovery and Data Mining, pp 1-30, AAAI/MIT Press
    • Fayyad, U., Piatesky-Shapiro, G. & Smyth P., From Data Mining to Knowledge Discovery: An Overview. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds), Advances in Knowledge Discovery and Data Mining, pp 1-30, AAAI/MIT Press, 1996.
    • (1996) From Data Mining to Knowledge Discovery: An Overview
    • Fayyad, U.1    Piatesky-Shapiro, G.2    Smyth, P.3
  • 82
    • 0027002165 scopus 로고
    • In proceedings of Tenth National Conference on Artificial Intelligence, pp. 104-110, Cambridge, MA: AAAI Press/MIT Press
    • Fayyad U., and Irani K. B., The attribute selection problem in decision tree generation. In proceedings of Tenth National Conference on Artificial Intelligence, pp. 104-110, Cambridge, MA: AAAI Press/MIT Press, 1992.
    • (1992) The attribute selection problem in decision tree generation
    • Fayyad, U.1    Irani, K.B.2
  • 83
    • 1942514832 scopus 로고    scopus 로고
    • In Claude Sammut and Achim Hoffmann, editors, Proceedings of the 19th International Conference on Machine Learn-ing, pp, Morgan Kaufmann, July
    • Ferri C, Flach P., and Hernandez-Orallo J., Learning Decision Trees Using the Area Under the ROC Curve. In Claude Sammut and Achim Hoffmann, editors, Proceedings of the 19th International Conference on Machine Learn-ing, pp. 139-146. Morgan Kaufmann, July 2002
    • (2002) Learning Decision Trees Using the Area Under the ROC Curve , pp. 139-146
    • Ferri, C.1    Flach, P.2    Hernandez-Orallo, J.3
  • 87
    • 0003909532 scopus 로고    scopus 로고
    • Nonparametric discrimination. Consistency properties. Technical Report 4, US Air Force School of Aviation Medicine. Randolph
    • Fix, E., and Hodges, J.L., Discriminatory analysis. Nonparametric discrimination. Consistency properties. Technical Report 4, US Air Force School of Aviation Medicine. Randolph
    • Discriminatory analysis
    • Fix, E.1    Hodges, J.L.2
  • 88
    • 85115710230 scopus 로고
    • In proceedings of the Multivariate Analysis, '66, P.R. Krishnaiah (Ed)
    • Field, TX, 1957. Fortier, J.J. and Solomon, H. 1996. Clustering procedures. In proceedings of the Multivariate Analysis, '66, P.R. Krishnaiah (Ed), pp. 493-506.
    • (1957) Clustering procedures , pp. 493-506
    • Field, T.X.1    Fortier, J.J.2    Solomon, H.3
  • 90
    • 0004021178 scopus 로고
    • G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, 1-27, AAAI Press, Menlo Park, California
    • Frawley W. J., Piatetsky-Shapiro G., and Matheus C. J., “Knowledge Discovery in Databases: An Overview, " G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, 1-27, AAAI Press, Menlo Park, California, 1991.
    • (1991) Knowledge Discovery in Databases: An Overview
    • Frawley, W.J.1    Piatetsky-Shapiro, G.2    Matheus, C.J.3
  • 92
    • 0030419058 scopus 로고    scopus 로고
    • In Machine Learning: Proceedings of the Thirteenth International Conference, pages
    • Freund Y. and Schapire R. E., Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 325-332, 1996.
    • (1996) Experiments with a new boosting algorithm , pp. 325-332
    • Freund, Y.1    Schapire, R.E.2
  • 94
    • 0002432565 scopus 로고
    • Multivariate Adaptive Regression Splines
    • Friedman, J. H., “Multivariate Adaptive Regression Splines”, The Annual Of Statistics, 19, 1-141, 1991.
    • (1991) The Annual Of Statistics , vol.19 , pp. 1-141
    • Friedman, J.H.1
  • 96
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1 -loss and the curse of dimensionality
    • Friedman, J.H. (1997b). On bias, variance, 0/1 -loss and the curse of dimensionality, Data Mining and Knowledge Discovery, 1: 1, 55-77, 1997.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , Issue.1 , pp. 55-77
    • Friedman, J.H.1
  • 97
    • 0016102310 scopus 로고
    • A Projection Pursuit Algorithm for Exploratory Data Analysis
    • Friedman, J.H. & Tukey, J.W., A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transactions on Computers, 23: 9, 881-889, 1973.
    • (1973) IEEE Transactions on Computers , vol.23 , Issue.9 , pp. 881-889
    • Friedman, J.H.1    Tukey, J.W.2
  • 100
    • 0031369629 scopus 로고    scopus 로고
    • In Proceeding of The 14th national Conference on Artificial Intelegence (AAAI-97), pp, Providence, RI. AAAI Press
    • Furnkranz, J., More efficient windowing, In Proceeding of The 14th national Conference on Artificial Intelegence (AAAI-97), pp. 509-514, Providence, RI. AAAI Press, 1997
    • (1997) More efficient windowing , pp. 509-514
    • Furnkranz, J.1
  • 101
    • 85115724272 scopus 로고
    • Modular Neural Net Systems
    • In (Ed) M.A. Arbib. The Handbook of Brain Theory and Neural Networks, Bradford Books/MIT Press
    • Gallinari, P., Modular Neural Net Systems, Training of. In (Ed) M.A. Arbib. The Handbook of Brain Theory and Neural Networks, Bradford Books/MIT Press, 1995.
    • (1995) Training of
    • Gallinari, P.1
  • 102
    • 78650167300 scopus 로고    scopus 로고
    • In C. Monard, editor, Advances on Artificial Intelligence -SBIA2000. LNAI 1952, Springer Verlag
    • Gama J., A Linear-Bayes Classifier. In C. Monard, editor, Advances on Artificial Intelligence -SBIA2000. LNAI 1952, pp 269-279, Springer Verlag, 2000
    • (2000) A Linear-Bayes Classifier , pp. 269-279
    • Gama, J.1
  • 106
    • 23044519492 scopus 로고    scopus 로고
    • RainForest -A Framework for Fast Decision Tree Construction of Large Datasets
    • Gehrke J., Ramakrishnan R., Ganti V., RainForest -A Framework for Fast Decision Tree Construction of Large Datasets, Data Mining and Knowledge Discovery, 4 (23) 127-162, 2000.
    • (2000) Data Mining and Knowledge Discovery , vol.4 , Issue.23 , pp. 127-162
    • Gehrke, J.1    Ramakrishnan, R.2    Ganti, V.3
  • 109
    • 0347808222 scopus 로고
    • MAID: A Honeywell 600 program for an automatised survey analysis
    • Gillo M. W., MAID: A Honeywell 600 program for an automatised survey analysis. Behavioral Science 17: 251-252, 1972.
    • (1972) Behavioral Science , vol.17 , pp. 251-252
    • Gillo, M.W.1
  • 110
    • 33749322782 scopus 로고    scopus 로고
    • Introduction to the Special Issue of on Meta-Learning
    • Giraud-Carrier Ch., Vilalta R., Brazdil R., Introduction to the Special Issue of on Meta-Learning. Machine Learning, 54 (3), 197-194, 2004.
    • (2004) Machine Learning , vol.54 , Issue.3 , pp. 197-194
    • Giraud-Carrier, C.1    Vilalta, R.2    Brazdil, R.3
  • 111
    • 0002410338 scopus 로고
    • Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, California: Lawrence Erlbaum Associates
    • Gluck, M. and Corter, J. (1985). Information, uncertainty, and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 283-287). Irvine, California: Lawrence Erlbaum Associates.
    • (1985) Information, uncertainty, and the utility of categories , pp. 283-287
    • Gluck, M.1    Corter, J.2
  • 118
    • 0041299747 scopus 로고    scopus 로고
    • Data Mining -reaching beyond statistics
    • Hand, D., Data Mining -reaching beyond statistics. Research in Official Stat. 1(2):5-17, 1998.
    • (1998) Research in Official Stat. , vol.1 , Issue.2 , pp. 5-17
    • Hand, D.1
  • 119
    • 0000856338 scopus 로고
    • The meta-Pi network -building distributed knowledge representations for robust multisource pattern-recognition
    • Hampshire, J. B., and Waibel, A. The meta-Pi network -building distributed knowledge representations for robust multisource pattern-recognition. Pattern Analyses and Machine Intelligence 14(7): 751-769, 1992.
    • (1992) Pattern Analyses and Machine Intelligence , vol.14 , Issue.7 , pp. 751-769
    • Hampshire, J.B.1    Waibel, A.2
  • 123
    • 27144536001 scopus 로고    scopus 로고
    • Extensions to the k-means algorithm for clustering large data sets with categorical values
    • Huang, Z., Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 1998
    • (1998) Data Mining and Knowledge Discovery , vol.2 , pp. 3
    • Huang, Z.1
  • 124
    • 0034655052 scopus 로고    scopus 로고
    • Decomposition in Automatic Generation of Petri Nets for Manufacturing System Control and Scheduling
    • He D. W., Strege B., Tolle H., and Kusiak A., Decomposition in Automatic Generation of Petri Nets for Manufacturing System Control and Scheduling. International Journal of Production Research, 38(6): 1437-1457, 2000
    • (2000) International Journal of Production Research , vol.38 , Issue.6 , pp. 1437-1457
    • He, D.W.1    Strege, B.2    Tolle, H.3    Kusiak, A.4
  • 126
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • Holte R. C, Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-90, 1993
    • (1993) Machine Learning , vol.11 , pp. 63-90
    • Holte, R.C.1
  • 133
    • 0028516150 scopus 로고
    • Nonparametric multivariate density estimation: A comparative study
    • Hwang J., Lay S., and Lippman A., Nonparametric multivariate density estimation: A comparative study. IEEE Transaction on Signal Processing, 42(10): 2795-2810, 1994.
    • (1994) IEEE Transaction on Signal Processing , vol.42 , Issue.10 , pp. 2795-2810
    • Hwang, J.1    Lay, S.2    Lippman, A.3
  • 134
    • 0001815269 scopus 로고
    • Constructing optimal binary decision trees is NP-complete
    • Hyafil L. and Rivest R.L., Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1):15-17, 1976
    • (1976) Information Processing Letters , vol.5 , Issue.1 , pp. 15-17
    • Hyafil, L.1    Rivest, R.L.2
  • 136
    • 85115667730 scopus 로고    scopus 로고
    • Data Clustering: A Survey
    • September
    • Jain, A.K. Murty, M.N. and Flynn, P.J. Data Clustering: A Survey. ACM Computing Surveys, Vol. 31, No. 3, September 1999.
    • (1999) ACM Computing Surveys , vol.31 , Issue.3
    • Murty, J.A.K.1    Flynn, P.J.2
  • 137
    • 0027634760 scopus 로고
    • A simplified neural network solution through problem decomposition: The case of Truck backer-upper
    • Jenkins R. and Yuhas, B. P. A simplified neural network solution through problem decomposition: The case of Truck backer-upper, IEEE Transactions on Neural Networks 4(4):718-722, 1993.
    • (1993) IEEE Transactions on Neural Networks , vol.4 , Issue.4 , pp. 718-722
    • Jenkins, R.1    Yuhas, B.P.2
  • 139
    • 0002655125 scopus 로고
    • A narmax model representation for adaptive control based on local model -Modeling
    • Johansen T. A. and Foss B. A., A narmax model representation for adaptive control based on local model -Modeling. Identification and Control, 13(1):25-39, 1992.
    • (1992) Identification and Control , vol.13 , Issue.1 , pp. 25-39
    • Johansen, T.A.1    Foss, B.A.2
  • 140
    • 2342647085 scopus 로고    scopus 로고
    • In D. Fisher and H. Lenz, editors, Learning From Data: Artificial Intelligence and Statistics V, Lecture Notes in Statistics, Chapter 36, , Springer-Verlag, New York
    • John G. H., Robust linear discriminant trees. In D. Fisher and H. Lenz, editors, Learning From Data: Artificial Intelligence and Statistics V, Lecture Notes in Statistics, Chapter 36, pp. 375-385. Springer-Verlag, New York, 1996.
    • (1996) Robust linear discriminant trees , pp. 375-385
    • John, G.H.1
  • 142
    • 85115684006 scopus 로고
    • Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338-345. Morgan Kaufmann, San Mateo
    • John G. H., and Langley P., Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338-345. Morgan Kaufmann, San Mateo, 1995.
    • (1995) Estimating Continuous Distributions in Bayesian Classifiers
    • John, G.H.1    Langley, P.2
  • 143
    • 0000262562 scopus 로고
    • Hierarchical mixtures of experts and the EM algorithm
    • Jordan, M. I., and Jacobs, R. A., Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214, 1994.
    • (1994) Neural Computation , vol.6 , pp. 181-214
    • Jordan, M.I.1    Jacobs, R.A.2
  • 144
    • 0001632132 scopus 로고
    • In Advances in Neural Information Processing Systems, J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds., vol. 4, Morgan Kaufmann Publishers, Inc., pp
    • Jordan, M. I., and Jacobs, R. A. Hierarchies of adaptive experts. In Advances in Neural Information Processing Systems, J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds., vol. 4, Morgan Kaufmann Publishers, Inc., pp. 985-992, 1992.
    • (1992) Hierarchies of adaptive experts , pp. 985-992
    • Jordan, M.I.1    Jacobs, R.A.2
  • 145
    • 33646388607 scopus 로고    scopus 로고
    • Second IEEE Internationa] Conference on Data Mining, IEEE Computer Society Press
    • Joshi, V. M., “On Evaluating Performance of Classifiers for Rare Classes”, Second IEEE Internationa] Conference on Data Mining, IEEE Computer Society Press, pp. 641-644, 2002
    • (2002) On Evaluating Performance of Classifiers for Rare Classes , pp. 641-644
    • Joshi, V.M.1
  • 146
    • 0016125209 scopus 로고
    • Patterns in Pattern Recognition: 1968-1974
    • IT-20
    • Kanal, L. N., “Patterns in Pattern Recognition: 1968-1974". IEEE Transactions on Information Theory IT-20, 6: 697-722, 1974
    • (1974) IEEE Transactions on Information Theory , vol.6 , pp. 697-722
    • Kanal, L.N.1
  • 148
    • 0001217510 scopus 로고
    • In Y. Dodge, editor, Statistical Data Analysis, based on the LI Norm, pp, Elsevier/North Holland, Amsterdam
    • Kaufman, L. and Rousseeuw, P.J., 1987, Clustering by Means of Medoids, In Y. Dodge, editor, Statistical Data Analysis, based on the LI Norm, pp. 405-416, Elsevier/North Holland, Amsterdam
    • (1987) Clustering by Means of Medoids , pp. 405-416
    • Kaufman, L.1    Rousseeuw, P.J.2
  • 150
    • 0000661829 scopus 로고
    • An exploratory technique for investigating large quantities of categorical data
    • Kass G. V., An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2): 119-127, 1980.
    • (1980) Applied Statistics , vol.29 , Issue.2 , pp. 119-127
    • Kass, G.V.1
  • 151
    • 0003269205 scopus 로고    scopus 로고
    • in J. Shavlik, ed., ‘Machine Learning: Proceedings of the Fifteenth International Conference’, Morgan Kaufmann Publishers, Inc., pp
    • Kearns M. and Mansour Y., A fast, bottom-up decision tree pruning algorithm with near-optimal generalization, in J. Shavlik, ed., ‘Machine Learning: Proceedings of the Fifteenth International Conference’, Morgan Kaufmann Publishers, Inc., pp. 269-277, 1998.
    • (1998) A fast, bottom-up decision tree pruning algorithm with near-optimal generalization , pp. 269-277
    • Kearns, M.1    Mansour, Y.2
  • 152
    • 0033075132 scopus 로고    scopus 로고
    • On the boosting ability of top-down decision tree learning algorithms
    • Kearns M. and Mansour Y., On the boosting ability of top-down decision tree learning algorithms. Journal of Computer and Systems Sciences, 58(1): 109-128, 1999.
    • (1999) Journal of Computer and Systems Sciences , vol.58 , Issue.1 , pp. 109-128
    • Kearns, M.1    Mansour, Y.2
  • 154
    • 0026995495 scopus 로고
    • in AAAI-92, Proceedings Ninth National Conference on Artificial Intelligence, AAAI Press/MIT Press
    • Kerber, R., 1992, ChiMerge: Descretization of numeric attributes, in AAAI-92, Proceedings Ninth National Conference on Artificial Intelligence, pp. 123-128, AAAI Press/MIT Press.
    • (1992) ChiMerge: Descretization of numeric attributes , pp. 123-128
    • Kerber, R.1
  • 156
    • 0034831822 scopus 로고    scopus 로고
    • A novel validity index for determination of the optimal number of clusters
    • Vol. E84-D, no.2
    • Kim, D.J., Park, Y.W. and Park,. A novel validity index for determination of the optimal number of clusters. IEICE Trans. Inf., Vol. E84-D, no.2 (2001), 281-285.
    • (2001) IEICE Trans. Inf. , pp. 281-285
    • Kim, D.J.1    Park, Y.W.2    Park3
  • 157
    • 84947386456 scopus 로고
    • Step-wise Clustering Procedures
    • King, B. Step-wise Clustering Procedures, J. Am. Stat. Assoc. 69, pp. 86-101, 1967.
    • (1967) J. Am. Stat. Assoc. , vol.69 , pp. 86-101
    • King, B.1
  • 158
    • 85115716110 scopus 로고    scopus 로고
    • Klosgen W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, Oxford University Press
    • Klosgen W. and Zytkow J. M., “KDD: The Purpose, Necessity and Chalanges”, Klosgen W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, pp. 1-9. Oxford University Press, 2002.
    • (2002) KDD: The Purpose, Necessity and Chalanges , pp. 1-9
    • Klosgen, W.1    Zytkow, J.M.2
  • 159
    • 84991254436 scopus 로고
    • in F. Bergadano and L. De Raedt, editors, Proc. European Conference on Machine Learning, Springer-Verlag
    • Kohavi, R., Bottom-up induction of oblivious read-once decision graphs, in F. Bergadano and L. De Raedt, editors, Proc. European Conference on Machine Learning, pp. 154-169, Springer-Verlag, 1994.
    • (1994) Bottom-up induction of oblivious read-once decision graphs , pp. 154-169
    • Kohavi, R.1
  • 162
    • 0002959696 scopus 로고    scopus 로고
    • In Feature Extraction, Construction and Selection: A Data Mining Perspective, H. Liu and H. Motoda (eds), Kluwer Academic Publishers
    • Kohavi R. and John G., The Wrapper Approach, In Feature Extraction, Construction and Selection: A Data Mining Perspective, H. Liu and H. Motoda (eds), Kluwer Academic Publishers, 1998.
    • (1998) The Wrapper Approach
    • Kohavi, R.1    John, G.2
  • 163
    • 0001290841 scopus 로고    scopus 로고
    • Glossary of Terms
    • Kohavi R., and Provost F., Glossary of Terms. Machine Learning 30(23): 271-274, 1998.
    • (1998) Machine Learning , vol.30 , Issue.23 , pp. 271-274
    • Kohavi, R.1    Provost, F.2
  • 164
    • 27844588525 scopus 로고    scopus 로고
    • In Klosgen W. and Zytkow J. M., editors, Handbook of Data Mining and Knowledge Discovery, chapter 16.1.3, pages 267-276. Oxford University Press
    • Kohavi R. and Quinlan J. R., Decision-tree discovery. In Klosgen W. and Zytkow J. M., editors, Handbook of Data Mining and Knowledge Discovery, chapter 16.1.3, pages 267-276. Oxford University Press, 2002.
    • (2002) Decision-tree discovery
    • Kohavi, R.1    Quinlan, J.R.2
  • 165
    • 0013109313 scopus 로고    scopus 로고
    • in R. Agrawal, P. Stolorz & G. Piatetsky-Shapiro, eds, ‘Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining’, AAAI Press, pp
    • Kohavi R. and Sommerfield D., Targeting business users with decision table classifiers, in R. Agrawal, P. Stolorz & G. Piatetsky-Shapiro, eds, ‘Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining’, AAAI Press, pp. 249-253, 1998.
    • (1998) Targeting business users with decision table classifiers , pp. 249-253
    • Kohavi, R.1    Sommerfield, D.2
  • 167
    • 0000670848 scopus 로고
    • Back propagation is sesitive to initial conditions
    • San Francisco, CA. Morgan Kaufmann
    • Kolen, J. F., and Pollack, J. B., Back propagation is sesitive to initial conditions. In Advances in Neural Information Processing Systems, Vol. 3, pp. 860-867 San Francisco, CA. Morgan Kaufmann, 1991.
    • (1991) In Advances in Neural Information Processing Systems , vol.3 , pp. 860-867
    • Kolen, J.F.1    Pollack, J.B.2
  • 169
    • 85031799549 scopus 로고
    • Proceedings of the Sixth European Working Session on Learning, pp, Porto, Portugal: SpringerVerlag
    • Kononenko, I., SemiNaive Bayes classifier, Proceedings of the Sixth European Working Session on Learning, pp. 206-219, Porto, Portugal: SpringerVerlag, 1991.
    • (1991) SemiNaive Bayes classifier , pp. 206-219
    • Kononenko, I.1
  • 171
    • 0002719797 scopus 로고
    • The Hungarian method for the assignment problem
    • Kuhn H. W., The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83-97, 1955
    • (1955) Naval Research Logistics Quarterly , vol.2 , pp. 83-97
    • Kuhn, H.W.1
  • 173
  • 174
    • 0034960598 scopus 로고    scopus 로고
    • Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing
    • Kusiak, A., Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing, IEEE Transactions on Electronics Packaging Manufacturing, 24(1): 44-50, 2001.
    • (2001) IEEE Transactions on Electronics Packaging Manufacturing , vol.24 , Issue.1 , pp. 44-50
    • Kusiak, A.1
  • 177
    • 0026191290 scopus 로고
    • A Novel Approach to Decomposition of Design Specifications and Search for Solutions
    • Kusiak, E. Szczerbicki, and K. Park, A Novel Approach to Decomposition of Design Specifications and Search for Solutions, International Journal of Production Research, 29(7): 1391-1406, 1991
    • (1991) International Journal of Production Research , vol.29 , Issue.7 , pp. 1391-1406
    • Szczerbicki, K.E.1    Park, K.2
  • 178
    • 0001977664 scopus 로고
    • in Proceedings of the AAAI Fall Symposium on Relevance, pp, AAAI Press,
    • Langley, P., Selection of relevant features in machine learning, in Proceedings of the AAAI Fall Symposium on Relevance, pp. 140-144, AAAI Press, 1994
    • (1994) Selection of relevant features in machine learning , pp. 140-144
    • Langley, P.1
  • 179
    • 0342809913 scopus 로고
    • in Working Notes of the AAAI-94 Workshop on Case-Based Reasoning, pp, Seattle, WA: AAAI Press
    • Langley, P. and Sage, S., Oblivious decision trees and abstract cases, in Working Notes of the AAAI-94 Workshop on Case-Based Reasoning, pp. 113-117, Seattle, WA: AAAI Press, 1994
    • (1994) Oblivious decision trees and abstract cases , pp. 113-117
    • Langley, P.1    Sage, S.2
  • 180
    • 0001901666 scopus 로고
    • in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA: Morgan Kaufmann
    • Langley, P. and Sage, S., Induction of selective Bayesian classifiers, in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 399-406. Seattle, WA: Morgan Kaufmann, 1994
    • (1994) Induction of selective Bayesian classifiers , pp. 399-406
    • Langley, P.1    Sage, S.2
  • 183
    • 85115693657 scopus 로고    scopus 로고
    • in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed), Kluwer Academic Publishers, pp
    • Last M., Kandel A., Data Mining for Process and Quality Control in the Semiconductor Industry, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed), Kluwer Academic Publishers, pp. 207-234, 2001.
    • (2001) Data Mining for Process and Quality Control in the Semiconductor Industry , pp. 207-234
    • Last, M.1    Kandel, A.2
  • 184
    • 85124125604 scopus 로고
    • In Machine Learning: Proceedings of the Eleventh Annual Conference, New Brunswick, New Jersey, Morgan Kaufmann
    • Lewis D., and Catlett J., Heterogeneous uncertainty sampling for supervised learning. In Machine Learning: Proceedings of the Eleventh Annual Conference, pp. 148-156, New Brunswick, New Jersey, Morgan Kaufmann, 1994.
    • (1994) Heterogeneous uncertainty sampling for supervised learning , pp. 148-156
    • Lewis, D.1    Catlett, J.2
  • 185
    • 85013879626 scopus 로고
    • In seventeenth annual international ACM SIGIR conference on research and development in information retrieval, pp
    • Lewis, D., and Gale, W., Training text classifiers by uncertainty sampling, In seventeenth annual international ACM SIGIR conference on research and development in information retrieval, pp. 3-12, 1994.
    • (1994) Training text classifiers by uncertainty sampling , pp. 3-12
    • Lewis, D.1    Gale, W.2
  • 186
    • 0022597806 scopus 로고
    • Tree classifier design with a Permutation statistic
    • Li X. and Dubes R. C, Tree classifier design with a Permutation statistic. Pattern Recognition 19:229-235, 1986.
    • (1986) Pattern Recognition , vol.19 , pp. 229-235
    • Li, X.1    Dubes, R.C.2
  • 187
    • 84898990837 scopus 로고    scopus 로고
    • Constructing Heterogeneous Committees via Input Feature Grouping
    • Vol, S.A. Solla, T.K. Leen and K.-R. Muller (eds), MIT Press
    • Liao Y., and Moody J., Constructing Heterogeneous Committees via Input Feature Grouping, in Advances in Neural Information Processing Systems, Vol.12, S.A. Solla, T.K. Leen and K.-R. Muller (eds), MIT Press, 2000.
    • (2000) Advances in Neural Information Processing Systems , vol.12
    • Liao, Y.1    Moody, J.2
  • 188
    • 0034274591 scopus 로고    scopus 로고
    • A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
    • Lim X., Loh W.Y., and Shih X., A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40:203-228, 2000.
    • (2000) Machine Learning , vol.40 , pp. 203-228
    • Lim, X.1    Loh, W.Y.2    Shih, X.3
  • 189
    • 0020497050 scopus 로고
    • Automatic classification of cervical cells using a binary tree classifier
    • Lin Y. K. and Fu K., Automatic classification of cervical cells using a binary tree classifier. Pattern Recognition, 16(1):69-80, 1983.
    • (1983) Pattern Recognition , vol.16 , Issue.1 , pp. 69-80
    • Lin, Y.K.1    Fu, K.2
  • 191
    • 0031312210 scopus 로고    scopus 로고
    • Split selection methods for classification trees
    • Loh W.Y., and Shih X., Split selection methods for classification trees. Statistica Sinica, 7: 815-840, 1997.
    • (1997) Statistica Sinica , vol.7 , pp. 815-840
    • Loh, W.Y.1    Shih, X.2
  • 192
    • 0042942219 scopus 로고    scopus 로고
    • Families of splitting criteria for classification trees
    • Loh W.Y. and Shih X., Families of splitting criteria for classification trees. Statistics and Computing 9:309-315, 1999.
    • (1999) Statistics and Computing , vol.9 , pp. 309-315
    • Loh, W.Y.1    Shih, X.2
  • 195
    • 85115704721 scopus 로고
    • Lopez de Mantras R., A distance-based attribute selection measure for decision tree induction, Machine Learning 6:81-92, 1991
    • (1991) Machine Learning , vol.6 , pp. 81-92
    • Lopez de Mantras, R.1
  • 196
    • 0032594843 scopus 로고    scopus 로고
    • Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification
    • Lu B.L., Ito M., Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification, IEEE Trans, on Neural Networks, 10(5):1244-1256, 1999
    • (1999) IEEE Trans, on Neural Networks , vol.10 , Issue.5 , pp. 1244-1256
    • Lu, B.L.1    Ito, M.2
  • 202
    • 84957000505 scopus 로고    scopus 로고
    • Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp
    • Maimon O. and Rokach L., “Improving supervised learning by feature decomposition”, Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp. 178-196, 2002.
    • (2002) Improving supervised learning by feature decomposition , pp. 178-196
    • Maimon, O.1    Rokach, L.2
  • 204
    • 85115716867 scopus 로고    scopus 로고
    • in Proceedings of the 13th Annual Conference on Computer Learning Theory, San Francisco, Morgan Kaufmann
    • Mansour, Y. and McAllester, D., Generalization Bounds for Decision Trees, in Proceedings of the 13th Annual Conference on Computer Learning Theory, pp. 69-80, San Francisco, Morgan Kaufmann, 2000.
    • (2000) Generalization Bounds for Decision Trees , pp. 69-80
    • Mansour, Y.1    McAllester, D.2
  • 207
    • 0031211834 scopus 로고    scopus 로고
    • An exact probability metric for decision tree splitting and stopping. An Exact Probability Metric for Decision Tree Splitting and Stopping
    • Martin J. K., An exact probability metric for decision tree splitting and stopping. An Exact Probability Metric for Decision Tree Splitting and Stopping, Ma-chine Learning, 28 (2-3):257-291, 1997.
    • (1997) Ma-chine Learning , vol.28 , Issue.2-3 , pp. 257-291
    • Martin, J.K.1
  • 209
    • 85115703481 scopus 로고    scopus 로고
    • In Proc. If the fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March
    • Mehta M., Agrawal R. and Rissanen J., SLIQ: A fast scalable classifier for data mining: In Proc. If the fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996.
    • (1996) SLIQ: A fast scalable classifier for data mining
    • Mehta, M.1    Agrawal, R.2    Rissanen, J.3
  • 210
    • 0013392343 scopus 로고    scopus 로고
    • in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp, San Diego, USA
    • Meretakis, D. and Wthrich, B., Extending Nave Bayes Classifiers Using Long Itemsets, in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 165-174, San Diego, USA, 1999.
    • (1999) Extending Nave Bayes Classifiers Using Long Itemsets , pp. 165-174
    • Meretakis, D.1    Wthrich, B.2
  • 212
    • 0000942050 scopus 로고
    • A theory and methodology of inductive learning
    • Michalski R. S., A theory and methodology of inductive learning. Artificial Intelligence, 20:111-161, 1983.
    • (1983) Artificial Intelligence , vol.20 , pp. 111-161
    • Michalski, R.S.1
  • 213
    • 0002602497 scopus 로고
    • in R. Michalski, J. Carbonnel and T. Mitchell, eds, Machine Learning: An Artificial Intelligence Approach, Kaufmann, Paolo Alto, CA, pp
    • Michalski R. S., Understanding the nature of learning: issues and research directions, in R. Michalski, J. Carbonnel and T. Mitchell, eds, Machine Learning: An Artificial Intelligence Approach, Kaufmann, Paolo Alto, CA, pp. 3-25, 1986.
    • (1986) Understanding the nature of learning: Issues and research directions , pp. 3-25
    • Michalski, R.S.1
  • 214
    • 0040292538 scopus 로고
    • A Multistrategy Approach, Vol. J. Morgan Kaufmann
    • Michalski R. S., and Tecuci G.. Machine Learning, A Multistrategy Approach, Vol. J. Morgan Kaufmann, 1994
    • (1994) Machine Learning
    • Michalski, R.S.1    Tecuci, G.2
  • 215
    • 84945291830 scopus 로고
    • in Proceedings of the European Conference on Machine Learning, pp, Springer-Verlag
    • Michie, D., Problem decomposition and the learning of skills, in Proceedings of the European Conference on Machine Learning, pp. 17-31, Springer-Verlag, 1995.
    • (1995) Problem decomposition and the learning of skills , pp. 17-31
    • Michie, D.1
  • 217
    • 79952785777 scopus 로고
    • An empirical comparison of pruning methods for decision tree induction
    • Mingers J., An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4(2):227-243, 1989.
    • (1989) Machine Learning , vol.4 , Issue.2 , pp. 227-243
    • Mingers, J.1
  • 218
    • 0003266733 scopus 로고
    • in Artificial Intelligence at MIT., Expanding Frontiers, Patrick H. Winston (Ed), Vol 1, MIT Press, 1990. Reprinted in AI Magazine
    • Minsky M., Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy, in Artificial Intelligence at MIT., Expanding Frontiers, Patrick H. Winston (Ed), Vol 1, MIT Press, 1990. Reprinted in AI Magazine, 1991.
    • (1991) Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy
    • Minsky, M.1
  • 221
    • 0003682772 scopus 로고
    • Technical Report CBM-TR-117, Rutgers University, Department of Computer Science, New Brunswick, NJ
    • Mitchell, T., The need for biases in learning generalizations. Technical Report CBM-TR-117, Rutgers University, Department of Computer Science, New Brunswick, NJ, 1980.
    • (1980) The need for biases in learning generalizations
    • Mitchell, T.1
  • 222
    • 0000672424 scopus 로고
    • Fast learning in networks of locally tuned units
    • Moody, J. and Darken, C, Fast learning in networks of locally tuned units. Neural Computations, 1(2):281-294, 1989.
    • (1989) Neural Computations , vol.1 , Issue.2 , pp. 281-294
    • Moody, J.1    Darken, C.2
  • 224
    • 21844514350 scopus 로고
    • Automatic construction of decision trees for classification
    • Muller W., and Wysotzki F., Automatic construction of decision trees for classification. Annals of Operations Research, 52:231-247, 1994.
    • (1994) Annals of Operations Research , vol.52 , pp. 231-247
    • Muller, W.1    Wysotzki, F.2
  • 225
    • 0020848951 scopus 로고
    • A survey of recent advances in hierarchical clustering algorithms which use cluster centers
    • Murtagh, F. A survey of recent advances in hierarchical clustering algorithms which use cluster centers. Comput. J. 26 354-359, 1984.
    • (1984) Comput. J. , vol.26 , pp. 354-359
    • Murtagh, F.1
  • 226
    • 0002431740 scopus 로고    scopus 로고
    • Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
    • Murthy S. K., Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery, 2(4):345-389, 1998.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.4 , pp. 345-389
    • Murthy, S.K.1
  • 228
    • 27844543336 scopus 로고
    • NP-completeness of problems of construction of optimal decision trees
    • Naumov G.E., NP-completeness of problems of construction of optimal decision trees. Soviet Physics: Doklady, 36(4):270-271, 1991.
    • (1991) Soviet Physics: Doklady , vol.36 , Issue.4 , pp. 270-271
    • Naumov, G.E.1
  • 230
    • 0003136237 scopus 로고
    • In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB94, Santiago, Chile, Sept), VLDB Endowment, Berkeley, CA, 144155
    • Ng, R. and Han, J. 1994. Very large data bases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB94, Santiago, Chile, Sept), VLDB Endowment, Berkeley, CA, 144155.
    • (1994) Very large data bases
    • Ng, R.1    Han, J.2
  • 231
    • 0039685357 scopus 로고
    • In Proceedings of the Second European Working Session on Learning, pages
    • Niblett T., Constructing decision trees in noisy domains. In Proceedings of the Second European Working Session on Learning, pages 67-78, 1987.
    • (1987) Constructing decision trees in noisy domains , pp. 67-78
    • Niblett, T.1
  • 233
    • 0010069683 scopus 로고
    • In Advances in Neural Information Processing Systems, R. P. Lipp-mann, J. E. Moody, and D. S. Touretzky, Eds., , Morgan Kaufmann Publishers Inc
    • Nowlan S. J., and Hinton G. E. Evaluation of adaptive mixtures of competing experts. In Advances in Neural Information Processing Systems, R. P. Lipp-mann, J. E. Moody, and D. S. Touretzky, Eds., vol. 3, pp. 774-780, Morgan Kaufmann Publishers Inc., 1991.
    • (1991) Evaluation of adaptive mixtures of competing experts , vol.3 , pp. 774-780
    • Nowlan, S.J.1    Hinton, G.E.2
  • 235
    • 0031513627 scopus 로고    scopus 로고
    • Modular neural networks for medical prognosis: Quantifying the benefits of combining neural networks for survival prediction
    • Ohno-Machado, L., and Musen, M. A. Modular neural networks for medical prognosis: Quantifying the benefits of combining neural networks for survival prediction. Connection Science 9, 1 (1997), 71-86.
    • (1997) Connection Science , vol.9 , Issue.1 , pp. 71-86
    • Ohno-Machado, L.1    Musen, M.A.2
  • 236
    • 0000551189 scopus 로고    scopus 로고
    • Popular Ensemble Methods: An Empirical Study
    • Opitz, D. and Maclin, R., Popular Ensemble Methods: An Empirical Study, Journal of Artificial Research, 11: 169-198, 1999.
    • (1999) Journal of Artificial Research , vol.11 , pp. 169-198
    • Opitz, D.1    Maclin, R.2
  • 237
    • 0025389210 scopus 로고
    • Boolean feature discovery in empirical learning
    • Pagallo, G. and Huassler, D., Boolean feature discovery in empirical learning. Machine Learning, 5(1): 71-99, 1990.
    • (1990) Machine Learning , vol.5 , Issue.1 , pp. 71-99
    • Pagallo, G.1    Huassler, D.2
  • 238
    • 0000717511 scopus 로고    scopus 로고
    • In Touretzky, D. S., Mozer, M. C, and Hes-selmo, M. E. (Eds). Advances in Neural Information Processing Systems, Vol., Cambridge, MA. MIT Press
    • Parmanto, B., Munro, P. W., and Doyle, H. R., Improving committee diagnosis with resampling techinques. In Touretzky, D. S., Mozer, M. C, and Hes-selmo, M. E. (Eds). Advances in Neural Information Processing Systems, Vol. 8, pp. 882-888 Cambridge, MA. MIT Press, 1996.
    • (1996) Improving committee diagnosis with resampling techinques , vol.8 , pp. 882-888
    • Parmanto, B.1    Munro, P.W.2    Doyle, H.R.3
  • 240
    • 33749353581 scopus 로고
    • Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition
    • Peng, F. and Jacobs R. A., and Tanner M. A., Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition, Journal of the American Statistical Association, 1995.
    • (1995) Journal of the American Statistical Association
    • Peng, F.1    Jacobs, R.A.2    Tanner, M.A.3
  • 243
    • 85115666561 scopus 로고    scopus 로고
    • Proceedings of the Second International Conference on Computational Intelligence and Multimedia Applications, pp, World Scientific, Australia
    • Perkowski, M., Jozwiak, L. and Mohamed, S., New approach to learning noisy Boolean functions, Proceedings of the Second International Conference on Computational Intelligence and Multimedia Applications, pp. 693-706, World Scientific, Australia, 1998.
    • (1998) New approach to learning noisy Boolean functions , pp. 693-706
    • Perkowski, M.1    Jozwiak, L.2    Mohamed, S.3
  • 244
    • 85011128600 scopus 로고
    • In Bergadano, F. and De Raedt, L. (Eds), Proceedings of the seventh European Conference on Machine Learning, pp. 242-256, Springer-Verlag
    • Pfahringer, B., Controlling constructive induction in CiPF, In Bergadano, F. and De Raedt, L. (Eds), Proceedings of the seventh European Conference on Machine Learning, pp. 242-256, Springer-Verlag, 1994.
    • (1994) Controlling constructive induction in CiPF
    • Pfahringer, B.1
  • 250
    • 0002515248 scopus 로고    scopus 로고
    • In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
    • Provost, F., Jensen, D. and Oates, T., 1999, Efficient Progressive Sampling, In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp.23-32.
    • (1999) Efficient Progressive Sampling , pp. 23-32
    • Provost, F.1    Jensen, D.2    Oates, T.3
  • 252
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan, J.R., Induction of decision trees. Machine Learning 1, 81-106, 1986.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 254
    • 0000600410 scopus 로고
    • J. Richards, ed., Machine Intelligence, V. 11, Oxford, England, Oxford Univ. Press, pp
    • Quinlan, J.R., Decision Trees and Multivalued Attributes, J. Richards, ed., Machine Intelligence, V. 11, Oxford, England, Oxford Univ. Press, pp. 305-318, 1988.
    • (1988) Decision Trees and Multivalued Attributes , pp. 305-318
    • Quinlan, J.R.1
  • 255
    • 85115714783 scopus 로고
    • In Segre, A. (Ed), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann
    • Quinlan, J. R., Unknown attribute values in induction. In Segre, A. (Ed), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann, 1989.
    • (1989) Unknown attribute values in induction
    • Quinlan, J.R.1
  • 256
    • 85115669210 scopus 로고
    • In Segre, A. (Ed), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann
    • Quinlan, J. R., Unknown attribute values in induction. In Segre, A. (Ed), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann, 1989.
    • (1989) Unknown attribute values in induction
    • Quinlan, J.R.1
  • 258
    • 0030370417 scopus 로고    scopus 로고
    • In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages
    • Quinlan, J. R., Bagging, Boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 725-730, 1996
    • (1996) Bagging, Boosting, and C4.5 , pp. 725-730
    • Quinlan, J.R.1
  • 259
    • 0024627518 scopus 로고
    • Inferring Decision Trees Using The Minimum Description Length Principle
    • Quinlan, J. R. and Rivest, R. L., Inferring Decision Trees Using The Minimum Description Length Principle. Information and Computation, 80:227-248, 1989.
    • (1989) Information and Computation , vol.80 , pp. 227-248
    • Quinlan, J.R.1    Rivest, R.L.2
  • 261
    • 0031197672 scopus 로고    scopus 로고
    • A new hybrid approach in combining multiple experts to recognize handwritten numerals
    • Rahman, A. F. R., and Fairhurst, M. C. A new hybrid approach in combining multiple experts to recognize handwritten numerals. Pattern Recognition Letters, 18: 781-790, 1997.
    • (1997) Pattern Recognition Letters , vol.18 , pp. 781-790
    • Rahman, A.F.R.1    Fairhurst, M.C.2
  • 262
    • 23044523130 scopus 로고    scopus 로고
    • PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
    • Rastogi, R., and Shim, K., PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning, Data Mining and Knowledge Discovery, 4(4):315-344, 2000.
    • (2000) Data Mining and Knowledge Discovery , vol.4 , Issue.4 , pp. 315-344
    • Rastogi, R.1    Shim, K.2
  • 263
    • 0032786303 scopus 로고    scopus 로고
    • Structurally Adaptive Modular Networks for Non-Stationary Environments
    • Ramamurti, V., and Ghosh, J., Structurally Adaptive Modular Networks for Non-Stationary Environments, IEEE Transactions on Neural Networks, 10 (1):152-160, 1999.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , Issue.1 , pp. 152-160
    • Ramamurti, V.1    Ghosh, J.2
  • 264
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • Rand, W. M., Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66: 846-850, 1971.
    • (1971) Journal of the American Statistical Association , vol.66 , pp. 846-850
    • Rand, W.M.1
  • 268
    • 33745826106 scopus 로고    scopus 로고
    • Klosgen W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, pages 185-192. Oxford University Press
    • Buczak A. L. and Ziarko W., “Stages of The Discovery Process”, Klosgen W. and Zytkow J. M. (Eds), Handbook of Data Mining and Knowledge Discovery, pages 185-192. Oxford University Press, 2002.
    • (2002) Stages of The Discovery Process
    • Buczak, A.L.1    Ziarko, W.2
  • 271
    • 56349143572 scopus 로고    scopus 로고
    • Proceedings of the First IEEE International Conference on Data Mining, IEEE Computer Society Press, pp
    • Rokach L. and Maimon O., “Theory and Application of Attribute Decomposition”, Proceedings of the First IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 473-480, 2001
    • (2001) Theory and Application of Attribute Decomposition , pp. 473-480
    • Rokach, L.1    Maimon, O.2
  • 273
    • 0019181932 scopus 로고
    • A combined non-parametric approach to feature selection and binary decision tree design
    • Rounds, E., A combined non-parametric approach to feature selection and binary decision tree design, Pattern Recognition 12, 313-317, 1980.
    • (1980) Pattern Recognition , vol.12 , pp. 313-317
    • Rounds, E.1
  • 274
    • 85115665012 scopus 로고
    • In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, D. Rumelhart and J. McClelland (eds) Cambridge, MA: MIT Press., pp
    • Rumelhart, D., G. Hinton and R. Williams, Learning internal representations through error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, D. Rumelhart and J. McClelland (eds) Cambridge, MA: MIT Press., pp 2540, 1986.
    • (1986) Learning internal representations through error propagation , pp. 2540
    • Rumelhart, D.1    Hinton, G.2    Williams, R.3
  • 277
    • 27144463192 scopus 로고    scopus 로고
    • On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
    • Kluwer Academic Publishers, Bosto
    • Salzberg. S. L., On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery, 1: 312-327, Kluwer Academic Publishers, Bosto, 1997.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 312-327
    • Salzberg, S.L.1
  • 278
    • 0001201757 scopus 로고
    • Some studies in machine learning using the game of checkers II: Recent progress
    • Samuel, A., Some studies in machine learning using the game of checkers II: Recent progress. IBM J. Res. Develop., 11:601-617, 1967.
    • (1967) IBM J. Res. Develop. , vol.11 , pp. 601-617
    • Samuel, A.1
  • 279
    • 0000245470 scopus 로고
    • Selecting a classification method by cross-validation
    • Schaffer, C, Selecting a classification method by cross-validation. Machine Learning 13(1):135-143, 1993.
    • (1993) Machine Learning , vol.13 , Issue.1 , pp. 135-143
    • Schaffer, C.1
  • 280
    • 37249060532 scopus 로고
    • In Proceedings of the 11th International Conference on Machine Learning: pp
    • Schaffer J., A Conservation Law for Generalization Performance. In Proceedings of the 11th International Conference on Machine Learning: pp. 259-265, 1993.
    • (1993) A Conservation Law for Generalization Performance , pp. 259-265
    • Schaffer, J.1
  • 282
    • 0036482614 scopus 로고    scopus 로고
    • On the complexity of computing and learning with multiplicative neural networks
    • Schmitt, M., On the complexity of computing and learning with multiplicative neural networks. Neural Computation 14: 2, 241-301, 2002.
    • (2002) Neural Computation , vol.14 , Issue.2 , pp. 241-301
    • Schmitt, M.1
  • 286
    • 0026359031 scopus 로고
    • A simulated annealing algorithm for the clustering problem
    • Selim, S. Z. AND Al-Sultan, K. 1991. A simulated annealing algorithm for the clustering problem. Pattern Recogn. 24, 10 (1991), 10031008.
    • (1991) Pattern Recogn , vol.24 , Issue.10 , pp. 10031008
    • Selim, S.Z.1    Al-Sultan, K.2
  • 288
    • 3142730058 scopus 로고    scopus 로고
    • On Learning Monotone DNF under Product Distributions
    • Servedio, R., On Learning Monotone DNF under Product Distributions. Information and Computation 193, pp. 57-74, 2004.
    • (2004) Information and Computation , vol.193 , pp. 57-74
    • Servedio, R.1
  • 289
    • 0028464458 scopus 로고
    • Design of multicategory, multifeature split decision trees using perceptron learning
    • Sethi, K., and Yoo, J. H., Design of multicategory, multifeature split decision trees using perceptron learning. Pattern Recognition, 27(7):939-947, 1994.
    • (1994) Pattern Recognition , vol.27 , Issue.7 , pp. 939-947
    • Sethi, K.1    Yoo, J.H.2
  • 291
    • 0003455515 scopus 로고
    • Turing Institute Press in association with Addison-Wesley Publishing Company
    • Shapiro, A. D., Structured induction in expert systems, Turing Institute Press in association with Addison-Wesley Publishing Company, 1987.
    • (1987) Structured induction in expert systems
    • Shapiro, A.D.1
  • 292
    • 0030372023 scopus 로고    scopus 로고
    • On combining artificial neural nets
    • Sharkey, A., On combining artificial neural nets, Connection Science, Vol. 8, pp.299-313, 1996.
    • (1996) Connection Science , vol.8 , pp. 299-313
    • Sharkey, A.1
  • 293
    • 0001906968 scopus 로고    scopus 로고
    • In Sharkey A. (Ed) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems, pp, Springer-Verlag
    • Sharkey, A., Multi-Net Iystems, In Sharkey A. (Ed) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems, pp. 1-30, Springer-Verlag, 1999.
    • (1999) Multi-Net Iystems , pp. 1-30
    • Sharkey, A.1
  • 294
    • 85115690785 scopus 로고    scopus 로고
    • Proc. 22nd Int. Conf. Very Large Databases, T. M. Vijayaraman and Alejandro P. Buchmann and C. Mohan and Nandlal L. Sarda (eds), 544-555, Morgan Kaufmann
    • Shafer, J. C, Agrawal, R. and Mehta, M., SPRINT: A Scalable Parallel Clas-sifier for Data Mining, Proc. 22nd Int. Conf. Very Large Databases, T. M. Vijayaraman and Alejandro P. Buchmann and C. Mohan and Nandlal L. Sarda (eds), 544-555, Morgan Kaufmann, 1996
    • (1996) SPRINT: A Scalable Parallel Clas-sifier for Data Mining
    • Shafer, J.C.1    Agrawal, R.2    Mehta, M.3
  • 295
    • 0025588128 scopus 로고
    • Multiple binary tree classifiers
    • Shilen, S., Multiple binary tree classifiers. Pattern Recognition 23(7): 757-763, 1990.
    • (1990) Pattern Recognition , vol.23 , Issue.7 , pp. 757-763
    • Shilen, S.1
  • 296
    • 38249014667 scopus 로고
    • Nonparametric classification using matched binary decision trees
    • Shilen, S., Nonparametric classification using matched binary decision trees. Pattern Recognition Letters 13: 83-87, 1992.
    • (1992) Pattern Recognition Letters , vol.13 , pp. 83-87
    • Shilen, S.1
  • 303
    • 85115713154 scopus 로고    scopus 로고
    • Proceedings of Intelligent Engineering Systems Through Artificial Neural Networks, 5-8 November 2000, St. Louis, Missouri, USA, pp
    • Strehl A. and Ghosh J., Clustering Guidance and Quality Evaluation Using Relationship-based Visualization, Proceedings of Intelligent Engineering Systems Through Artificial Neural Networks, 5-8 November 2000, St. Louis, Missouri, USA, pp 483-488.
    • Clustering Guidance and Quality Evaluation Using Relationship-based Visualization , pp. 483-488
    • Strehl, A.1    Ghosh, J.2
  • 305
    • 0032157958 scopus 로고    scopus 로고
    • Learning from examples and membership queries with structured determinations
    • Tadepalli, P. and Russell, S., Learning from examples and membership queries with structured determinations. Machine Learning, 32(3), pp. 245-295, 1998.
    • (1998) Machine Learning , vol.32 , Issue.3 , pp. 245-295
    • Tadepalli, P.1    Russell, S.2
  • 306
    • 0000687440 scopus 로고
    • Block diagrams and splitting criteria for classification trees
    • Taylor P. C, and Silverman, B. W., Block diagrams and splitting criteria for classification trees. Statistics and Computing, 3(4):147-161, 1993.
    • (1993) Statistics and Computing , vol.3 , Issue.4 , pp. 147-161
    • Taylor, P.C.1    Silverman, B.W.2
  • 308
    • 0028529307 scopus 로고
    • Knowledge-based artificial neural networks
    • Towell, G. Shavlik, J., Knowledge-based artificial neural networks, Artificial Intelligence, 70: 119-165, 1994.
    • (1994) Artificial Intelligence , vol.70 , pp. 119-165
    • Shavlik, T.G.J.1
  • 309
    • 0000442861 scopus 로고
    • In Tesauro, G., Touretzky, D., & Leen, T. (Eds), Advances in Neural Information Processing Systems, volume, The MIT Press
    • Tresp, V. and Taniguchi, M. Combining estimators using non-constant weighting functions. In Tesauro, G., Touretzky, D., & Leen, T. (Eds), Advances in Neural Information Processing Systems, volume 7: pp. 419-426, The MIT Press, 1995.
    • (1995) Combining estimators using non-constant weighting functions , vol.7 , pp. 419-426
    • Tresp, V.1    Taniguchi, M.2
  • 310
    • 33646516485 scopus 로고
    • Possible Generalization of Boltzmann-Gibbs Statistics
    • Tsallis C, Possible Generalization of Boltzmann-Gibbs Statistics, J. Stat.Phys., 52, 479-487, 1988.
    • (1988) J. Stat.Phys. , vol.52 , pp. 479-487
    • Tsallis, C.1
  • 313
    • 85115730045 scopus 로고    scopus 로고
    • In Kargupta, H. and Chan P., eds, Advances in Distributed and Parallel Knowledge Discovery, pp, AAAI/MIT Press
    • Turner, K., and Ghosh J., Robust Order Statistics based Ensembles for Distributed Data Mining. In Kargupta, H. and Chan P., eds, Advances in Distributed and Parallel Knowledge Discovery, pp. 185-210, AAAI/MIT Press, 2000.
    • (2000) Robust Order Statistics based Ensembles for Distributed Data Mining , pp. 185-210
    • Turner, K.1    Ghosh, J.2
  • 315
    • 0019999522 scopus 로고
    • Graph-theoretical clustering, based on limited neighborhood sets
    • vol
    • Urquhart, R. Graph-theoretical clustering, based on limited neighborhood sets. Pattern recognition, vol. 15, pp. 173-187, 1982.
    • (1982) Pattern recognition , vol.15 , pp. 173-187
    • Urquhart, R.1
  • 316
    • 84945766475 scopus 로고
    • Perceptron trees: A case study in hybrid concept representations
    • Utgoff, P. E., Perceptron trees: A case study in hybrid concept representations. Connection Science, 1(4):377-391, 1989.
    • (1989) Connection Science , vol.1 , Issue.4 , pp. 377-391
    • Utgoff, P.E.1
  • 317
    • 77952642202 scopus 로고
    • Incremental induction of decision trees
    • Utgoff, P. E., Incremental induction of decision trees. Machine Learning, 4:161-186, 1989.
    • (1989) Machine Learning , vol.4 , pp. 161-186
    • Utgoff, P.E.1
  • 318
    • 0031246271 scopus 로고    scopus 로고
    • Decision tree induction based on efficient tree restructuring
    • Utgoff, P. E., Decision tree induction based on efficient tree restructuring. Machine Learning 29 (1):5-44, 1997.
    • (1997) Machine Learning , vol.29 , Issue.1 , pp. 5-44
    • Utgoff, P.E.1
  • 320
    • 0021518106 scopus 로고
    • Communications of the ACM 1984
    • Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM 1984, pp. 1134-1142.
    • (1984) A theory of the learnable , pp. 1134-1142
    • Valiant, L.G.1
  • 321
    • 0342938790 scopus 로고
    • Information Retrieval
    • ISBN 0-408-70929-4
    • Van Rijsbergen, C. J., Information Retrieval. Butterworth, ISBN 0-408-70929-4, 1979.
    • (1979) Butterworth
    • Van Rijsbergen, C.J.1
  • 325
    • 85115733130 scopus 로고    scopus 로고
    • In A. Gam-merman (ed), Computational Learning and Probabilistic Reasoning, pp, Wiley
    • Wallace, C. S., MML Inference of Predictive Trees, Graphs and Nets. In A. Gam-merman (ed), Computational Learning and Probabilistic Reasoning, pp 43-66, Wiley, 1996.
    • (1996) MML Inference of Predictive Trees, Graphs and Nets , pp. 43-66
    • Wallace, C.S.1
  • 327
    • 0008137501 scopus 로고
    • In Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, pages
    • Wallace C. S. and Dowe D. L., Intrinsic classification by mml -the snob program. In Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, pages 37-44, 1994.
    • (1994) Intrinsic classification by mml -the snob program , pp. 37-44
    • Wallace, C.S.1    Dowe, D.L.2
  • 330
    • 84944178665 scopus 로고
    • Hierarchical grouping to optimize an objective function
    • Ward, J. H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58:236-244, 1963.
    • (1963) Journal of the American Statistical Association , vol.58 , pp. 236-244
    • Ward, J.H.1
  • 331
    • 0029446195 scopus 로고
    • Nonlinear gated experts for time-series -discovering regimes and avoiding overfitting
    • Weigend, A. S., Mangeas, M., and Srivastava, A. N. Nonlinear gated experts for time-series -discovering regimes and avoiding overfitting. International Journal of Neural Systems 6(5):373-399, 1995.
    • (1995) International Journal of Neural Systems , vol.6 , Issue.5 , pp. 373-399
    • Weigend, A.S.1    Mangeas, M.2    Srivastava, A.N.3
  • 332
    • 0026692226 scopus 로고
    • Stacked Generalization
    • Vol, Pergamon Press
    • Wolpert, D.H., Stacked Generalization, Neural Networks, Vol. 5, pp. 241-259, Pergamon Press, 1992.
    • (1992) Neural Networks , vol.5 , pp. 241-259
    • Wolpert, D.H.1
  • 334
    • 0000459353 scopus 로고    scopus 로고
    • The lack of a priori distinctions between learning algorithms
    • Wolpert, D. H., “The lack of a priori distinctions between learning algorithms, " Neural Computation 8: 1341-1390, 1996.
    • (1996) Neural Computation , vol.8 , pp. 1341-1390
    • Wolpert, D.H.1
  • 342
    • 8344279588 scopus 로고    scopus 로고
    • in Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron (Eds): Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 9th International Conference, RSFDGrC, Chongqing, China, Proceedings. Lecture Notes in Computer Science 2639
    • Zhou, Z. H., and Tang, W., Selective Ensemble of Decision Trees, in Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron (Eds): Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 9th International Conference, RSFDGrC, Chongqing, China, Proceedings. Lecture Notes in Computer Science 2639, pp.476-483, 2003.
    • (2003) Selective Ensemble of Decision Trees , pp. 476-483
    • Zhou, Z.H.1    Tang, W.2
  • 343
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • Zhou, Z. H., Wu J., Tang W., Ensembling neural networks: many could be better than all. Artificial Intelligence 137: 239-263, 2002.
    • (2002) Artificial Intelligence , vol.137 , pp. 239-263
    • Zhou, Z.H.1    Wu, J.2    Tang, W.3
  • 345
    • 0344977995 scopus 로고    scopus 로고
    • in Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), San Francisco, CA, pp
    • Zupan, B., Bratko, I., Bohanec, M. and Demsar, J., 2000, Induction of concept hierarchies from noisy data, in Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), San Francisco, CA, pp. 1199-1206.
    • (2000) Induction of concept hierarchies from noisy data , pp. 1199-1206
    • Zupan, B.1    Bratko, I.2    Bohanec, M.3    Demsar, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.